LingBot-VA combines video world modeling with policy learning via Mixture-of-Transformers, closed-loop rollouts, and asynchronous inference to improve robot manipulation in simulation and real settings.
Particle-grid neural dynamics for learning deformable object models from rgb-d videos
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TAG-K combines greedy randomized Kaczmarz row selection with tail averaging to deliver faster convergence and noise robustness for online inertial parameter estimation in robotics.
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Causal World Modeling for Robot Control
LingBot-VA combines video world modeling with policy learning via Mixture-of-Transformers, closed-loop rollouts, and asynchronous inference to improve robot manipulation in simulation and real settings.
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TAG-K: Tail-Averaged Greedy Kaczmarz for Computationally Efficient and Performant Online Inertial Parameter Estimation
TAG-K combines greedy randomized Kaczmarz row selection with tail averaging to deliver faster convergence and noise robustness for online inertial parameter estimation in robotics.